Prediction of protein folding rates from the amino acid sequence-predicted secondary structure

Ivankov, Dmitry N.; Finkelstein, Alan

doi:10.1073/pnas.0402659101

Cited by 182 publications

(227 citation statements)

References 28 publications

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“…Much work on the folding rate prediction was published in the pureempirical approach. They include the prediction model based on amino acid sequence [18,2628], based on tertiary structure [15,29] and based on secondary structure [17], etc. The prediction accuracy is generally dependent on the size of database.…”

Section: Results Of Statistical Analysis Of 65 Two-state Protein Foldmentioning

confidence: 99%

Statistical analyses of protein folding rates from the view of quantum transition

Luo

2014

Sci. China Life Sci.

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Understanding protein folding rate is the primary key to unlock the fundamental physics underlying protein structure and its folding mechanism. Especially, the temperature dependence of the folding rate remains unsolved in the literature. Starting from the assumption that protein folding is an event of quantum transition between molecular conformations, we calculated the folding rate for all two-state proteins in a database and studied their temperature dependencies. The non-Arrhenius temperature relation for 16 proteins, whose experimental data had previously been available, was successfully interpreted by comparing the Arrhenius plot with the first-principle calculation. A statistical formula for the prediction of two-state protein folding rate was proposed based on quantum folding theory. The statistical comparisons of the folding rates for 65 two-state proteins were carried out, and the theoretical vs. experimental correlation coefficient was 0.73. Moreover, the maximum and the minimum folding rates given by the theory were consistent with the experimental results. quantum folding, protein folding rate, temperature dependence, number of torsion mode, folding free energy Citation:Lv J, Luo LF. Statistical analyses of protein folding rates from the view of quantum transition. Sci China Life Sci, 2014Sci, , 57: 1197Sci, -1212Sci, , doi: 10.1007 It is well known that a protein chain can spontaneously fold into its unique native structure [1,2]. To paraphrase Levinthal's paradox, if a protein were to attain its correctly folded configuration by sequentially sampling all the possible conformations, it would require a period of time longer than the age of the universe to arrive at its correct native conformation [3]. Apart from theory, it is a fact that experimentally measured times for spontaneous folding of single-domain globular proteins range from microseconds [46] to tens of minutes [7]. Thus, how configurations of proteins are determined and what makes them fold so quickly are questions that constitute a longstanding puzzle in molecular biology. While two prominent models have been proposed to study the protein folding mechanism, folding nucleus [8,9] and folding tunnel [1014], the importance of topology and contact order in protein folding has been recognized over the last 15 years, and many new models to predict the protein folding rate have been published [1529]. Curiously, it is notable that the rate at which proteins fold is highly sensitive to temperature, showing non-Arrhenius behavior, i.e., the temperature dependence of the rate constant is, in fact, not exponential for these reactions. The nonlinearity of logarithm folding rate on temperature 1/T has been conventionally interpreted by the nonlinear temperature dependence of the configurational diffusion constant on rough energy landscapes [30] or by the temperature dependence of hydrophobic interaction [31,32]. Another model was proposed more recently to interpret the difference between folding and unfolding by introducing the number of dena...

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Section: Results Of Statistical Analysis Of 65 Two-state Protein Foldmentioning

confidence: 99%

Statistical analyses of protein folding rates from the view of quantum transition

Luo

2014

Sci. China Life Sci.

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“…Similarly, there is a wide spread in the folding times as well [4,5,6]. The rates of folding vary by nearly nine orders of magnitude.…”

Section: Introductionmentioning

confidence: 99%

“…Sometime ago it was shown theoretically that the folding time ,τ F , should depend on N [7,8,9] but only recently has experimental data confirmed this prediction [4,6,10,11,12]. It has been…”

Section: Introductionmentioning

confidence: 99%

Effect of Finite Size on Cooperativity and Rates of Protein Folding

Kouza

O’Brien

et al. 2005

J. Phys. Chem. A

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We analyze the dependence of cooperativity of the thermal denaturation transition and folding rates of globular proteins on the number of amino acid residues, N , using lattice models with side chains, off-lattice Go models and the available experimental data. A dimensionless measure of cooperativity,The results of simulations and the analysis of experimental data further confirm the earlier prediction that ζ is universal with ζ = 1 + γ, where exponent γ characterizes the susceptibility of a self-avoiding walk. This finding suggests that the structural characteristics in the denaturated state are manifested in the folding cooperativity at the transition temperature. The folding rates k F for the Go models and a dataset of 69 proteins can be fit using k F = k 0 F exp(−cN β ). Both β = 1/2 and 2/3 provide a good fit of the data. We find that k F = k 0 F exp(−cN 1 2 ), with the average (over the dataset of proteins) k 0 F ≈ (0.2µs) −1 and c ≈ 1.1, can be used to estimate folding rates to within an order of magnitude in most cases. The minimal models give identical N dependence with c ≈ 1. The prefactor for off-lattice Go models is nearly four orders of magnitude larger than the experimental value.

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“…For example, mutations in the folding nucleus of CI2 did not change its contact order, but result in a three order magnitude increase in the folding rate [90,121,122]. Sequence-based prediction of folding rates has also been proposed and was found to be of comparable performance to that of contact order [123,124]. Thus, there is still a debate as to whether structure-based or sequence-based prediction is a more reliable predictor of folding rates [123,124].…”

Section: Protein Folding Kineticsmentioning

confidence: 99%

“…Sequence-based prediction of folding rates has also been proposed and was found to be of comparable performance to that of contact order [123,124]. Thus, there is still a debate as to whether structure-based or sequence-based prediction is a more reliable predictor of folding rates [123,124].…”

Section: Protein Folding Kineticsmentioning

confidence: 99%

Protein folding: Then and now

Chen

Ding

Nie

et al. 2008

Archives of Biochemistry and Biophysics

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Over the past three decades the protein folding field has undergone monumental changes. Originally a purely academic question, how a protein folds has now become vital in understanding diseases and our abilities to rationally manipulate cellular life by engineering protein folding pathways. We review and contrast past and recent developments in the protein folding field. Specifically, we discuss the progress in our understanding of protein folding thermodynamics and kinetics, the properties of evasive intermediates, and unfolded states. We also discuss how some abnormalities in protein folding lead to protein aggregation and human diseases.

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Prediction of protein folding rates from the amino acid sequence-predicted secondary structure

Cited by 182 publications

References 28 publications

Statistical analyses of protein folding rates from the view of quantum transition

Statistical analyses of protein folding rates from the view of quantum transition

Effect of Finite Size on Cooperativity and Rates of Protein Folding

Protein folding: Then and now

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